1. Field of the Invention
A vector neural network (VNN) is a network of interconnected neurons with a topology which supports the propagation of activations in several different directions through the network. The network topology is determined by the transition mapping. The weight associated with each interconnect represents the neuron's contribution to the activation level of a downstream or subsequent neuron. The transfer function can be linear or non-linear.
The above-identified cross-reference disclosed applying neural network technology to a plot/track association problem. Plot/track association in a track-while-scan operation consists of assigning radar plots to predicted track positions; an important feature of all track-while-scan systems.
It is necessary, however, prior to assigning the radar plots to predicted track positions that the targets first are detected.
The present invention applies neural network technology to detect a target by utilizing mosaic sensors and a track-before-detect approach. This is particularly necessary for low signal-to-noise ratio (SNR) detection of point source targets. The general concensus in the literature is that very dim targets (SNR&lt;1) cannot be detected by merely assembling trajectories based on threshold frames, even after using optimal SNR-enhancement filtering. Applying thresholding separately to each frame irreversibly discards extremely valuable information, and post-assembling trajectories cannot recoup the lost information (see Y. Barniv, "Dynamic Programming Algorithm for Detecting Dim Moving Targets"; Multi-Target Multi-Sensor Tracking: Advanced Applications, Y. Bar-Shalom (Editor), Artech House, 1990). One alternative is to postulate the entire trajectory of the target, integrate the target's signal along its entire trajectory, and threshold the integrated signal which would have significantly higher SNR. The complexity of the problem lies in the fact that the trajectories are unknown and the number of targets is unknown. The optimal detection solution is an exhaustive search of all possible trajectories. For example, assuming that all trajectories move in a straight line, the optimal detection could be performed by passing the data contained in all frames through a bank of matched filters (templates) which describe all possible straight lines. However, the exhaustive trajectory search, although optimal, is computationally intractable.
2. Description of the Related Art
Artificial neural networks are extremely powerful processing systems comprised of a plurality of simple processing elements often configured in layers and extensively interconnected. Artificial neural networks are attempts at processing architectures similar to naturally occurring, biological ones which solve problems that have not yielded to traditional computer methodologies and architectures. The name "neural network" derives from the biological "neuron" which is what each simple processor is called. Each artificial neuron operates in some fashion analogous to its biological counter part, namely generating an output signal which is a function of the weighted sum of the input signals it receives from neighboring neurons, with which it is interconnected. The weighted sum is passed through a "transfer" function to form the neuron's output. The weight of each input link describes the relative contribution of the line's input in computing the neuron's next state. A zero weight indicates that there is effectively no contribution. A negative weight indicates an inhibitory relationship. A positive weight shows an excitatory relationship.
In recent years, the prior art reveals a plethora of different neural network architectures that have been propounded by researchers; the most popular being backward propagation or "back-prop". Back-prop networks consist of an input layer, an output layer and one or more hidden layers to account for non-linearities. The prior art network "learns" by receiving a succession of known inputs and corresponding outputs, and by measuring the difference between the known output it should generate and what it actually produced. This difference is considered to be the error which the network seeks to minimize through appropriately adjusting the internal weights by propagating these errors backward. Thus by repeated "training" sequences, the neural network is "trained" and it converges on a set of appropriate weights and thresholds to be utilized in actual applications; those where the corresponding outputs are no longer known.
The vector neural network (VNN) of the present invention has an architecture which does utilize neurons but it is other than that of a back-prop neural network. The VNN is not "trained". The weights in the VNN are control parameters which are selected depending on SNR. The prior art "back-prop" network propagates errors backward during the "training" process, while the VNN utilizes backflow to integrate energy in order to perform energy balancing (for example, to detect maneuvers) during actual implementation. The VNN has a linear transfer function and an extremely sparse interconnected network. Further, there are no hidden layers.